secml: Secure and explainable machine learning in Python

• Published in: SoftwareX Vol. 18; p. 101095
• Main Authors: Pintor, Maura, Demetrio, Luca, Sotgiu, Angelo, Melis, Marco, Demontis, Ambra, Biggio, Battista
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.06.2022; Elsevier
• Subjects: Adversarial attacks; Explainability; Machine learning; Python3; Security; Security; Explainability; Machine learning; Python3; Adversarial attacks
• ISSN: 2352-7110; 2352-7110
• DOI: 10.1016/j.softx.2022.101095

We present secml, an open-source Python library for secure and explainable machine learning. It implements the most popular attacks against machine learning, including test-time evasion attacks to generate adversarial examples against deep neural networks and training-time poisoning attacks against support vector machines and many other algorithms. These attacks enable evaluating the security of learning algorithms and the corresponding defenses under both white-box and black-box threat models. To this end, secml provides built-in functions to compute security evaluation curves, showing how quickly classification performance decreases against increasing adversarial perturbations of the input data. secml also includes explainability methods to help understand why adversarial attacks succeed against a given model, by visualizing the most influential features and training prototypes contributing to each decision. It is distributed under the Apache License 2.0 and hosted at https://github.com/pralab/secml.